Localization and/or navigation systems have been gained interest in recent years due to the explosion in the number of networked smart devices and technologies. For example, a localization system can provide important information to a first responder regarding the location of a user in a shopping mall, museum, or a stadium. Location information can also help service providers in identifying coverage holes and traffic hotspots when deploying networks such as 4G Long Term Evolution (“LTE”) small cell networks.
A recent trend in the area of localization, and particularly indoor localization, is to use a standard, low-cost, and already deployed network infrastructure. Instead of spending resources in deploying dedicated network infrastructure for localization purposes, the focus is to design solutions that can be integrated with existing network infrastructure. Some examples of network infrastructures suitable for indoor (or outdoor, for that matter) localization of a user device include the WiFi, Bluetooth, and small cell network infrastructures. Trilateration and fingerprinting are examples of popular algorithms that are employed to estimate an indoor location of a user device (where, for example GPS based systems may be obstructed). Fingerprinting and trilateration may also be combined to improve indoor localization accuracy. The trilateration approach estimates the position of a user device by computing distances from multiple reference points. The distances from a reference point can be computed based on Time of Arrival (“TOA”) or received signal strength information. In order to enable 2-D positioning, at least three non-collocated and non-collinear reference points are acquired and are applied to linear equations to estimate the location of a user device.
Fingerprinting-based localization typically includes an offline and an online phase. The offline phase includes building a Radio Frequency (“RF”) signature map for a given geographical area, such as an area of a building, museum, or an arena. In the online localization phase, the real time received signal strength of signals received/transmitted by the user device is compared with the RF signature map built in the offline phase to compute an estimated location of the user's device.
The time and effort required to build an RF signature map of a geographical area during the offline phase of the fingerprinting based localization system have prompted research into simultaneous localization and mapping systems. However, although the effort to build the RF signature map is reduced in such systems, the performance is typically less than desired for most practical indoor applications.